A digital video camera, hereafter referred to simply as a camera, carries out imaging to produce a video signal in the form of a time sequence of images, each image represented by a two dimensional set of picture elements, called pixels. A camera includes a photosensitive medium, e.g., an image sensor, optics to focus a light image on to the photosensitive medium, and circuitry to convert the light image to the digital signals corresponding to the time sequence of images. A camera accepts one or more imaging parameters that control the imaging. Two commonly used imaging parameters are exposure and color balance; the latter sometimes called white balance. One manner of controlling exposure controls the total amount of light that can reach the photosensitive medium of the camera. Color balance enables adjustment of the relative intensities of primary colors, e.g., red, green, and blue, or RGB such that specific colors—particularly neutral colors, e.g. white, are rendered correctly in produced images.
A camera may have one or more elements configured to (i) measure one or more characteristics of the lighting within its field of view and (ii) adjust, e.g., automatically adjust imaging parameters according to the measured characteristics. For example, a camera may have automatic exposure control to adjust such parameters as signal integration times of the camera's one or more image sensors according to the exposure to ensure the signal is within a dynamic range. Some color cameras, e.g., color cameras that have multiple sensors, e.g., red-green-blue (“RGB”) sensors to produce multiple signal channels, one per color, include automatic white balance control to adjust relative gains used in each color channel according to a measure of the hue of lighting, e.g., the color temperature of lighting as measured by one or more sensors included in the camera. Many methods are known for performing automatic exposure and automatic white balance functions. Automatic exposure control, automatic white balance control, and automatic focus are sometimes referred to as 3A.
In many applications, e.g., video conferencing and video surveillance, the output of a camera may be coupled to a video encoder that compresses the images under a constant-bit-rate (“CBR”) limitation, in order to transmit the video data over a network connection. For example, a camera producing images with a spatial resolution of 1920×1080 pixels and at a rate of 30 images per second may be connected to a video encoder that encodes the sequence of images under a constant bit-rate of 4 Mega bits per second, or 4 Mbps.
Compression of images by a video encoder is commonly based on prediction. Data of a currently to-be-encoded image or portion thereof is predicted with reference to one or more previously processed images or portions thereof. The differences between the actual and predicted data, called residuals, are then encoded. In general, the larger differences between the currently to-be-encoded image and the reference images, the more likely a larger number of bits to be consumed to encode the image. Under a constant bit-rate, this also means that a lower-quality image is more likely to be generated by the video encoder, when the encoded image is decoded and displayed.
Existing methods of adjusting imaging parameters of a camera, such as those automatic exposure and automatic white balance functions, are based on detection of a change of lighting. None of the methods, however, takes into account the impact such adjustments have on a video encoder when the output of the camera is coupled to the encoder. More specifically, adjusting imaging parameters may result in a disruptive change of pixel values in the subsequent images produced by the camera, and consequently cause degradation of image quality to the encoder under a constrained bit-rate. Such degradation of one image may be extended to more subsequent images due to the predictive nature of compression.
The approaches described in this section are approaches that could be pursued, but not necessarily approaches that have been previously conceived or pursued. Therefore, unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, issues identified with respect to one or more approaches should not assume to have been recognized in any prior art on the basis of this section, unless otherwise indicated.